1. Comments on Rare Variants Analyses Ryo Yamada Kyoto University 2012/08/27 IBC2012@Kobe, Japan

2. Many difficulties to detect true signals in rare variant analyses Type 1 error, Power-control, Various Statistics with/without Weights, Data quality, Data load, Platform variations, Missing data, Permutations, Allele freq., Rares with Commons, Replication, Neutral or not,

3. Many difficulties to detect true signals in rare variant analyses Type 1 error, Power-control, Various Statistics with/without Weights, Data quality, Data load, Platform variations, Missing data, Permutations, Allele freq., Replication, Rares with Commons integrating all variations, So many....

4. Many difficulties to detect true signals in rare variant analyses My discussing points – Some problems in rare variant analyses are the unsolved problems in common variant analyses.

5. Many difficulties to detect true signals in rare variant analyses My discussing points – Some problems in rare variant analyses are the unsolved problems in common variant analyses.

6. Many difficulties to detect true signals in rare variant analyses My discussing points – Some problems in rare variant analyses are the unsolved problems in common variant analyses. – Changes in genetic studies along with rare variant analyses Next-generation sequencing technologies-driven changes

7. Common Variant Analyses Pre kit-GWAS ~2005 – Exonic region-dominant kit-GWAS 2005 ~ – Genome-wide

8. Common Variant Analyses Pre kit-GWAS ~2005 – Exonic region-dominant kit-GWAS 2005 ~ – Genome-wide Exome Whole-genome sequencing Rare Variant Analyses

9. Common Variant Analyses Pre kit-GWAS ~2005 – Exonic region-dominant kit-GWAS 2005 ~ – Genome-wide Exome Whole-genome sequencing Rare Variant Analyses déjà vu

10. What we can learn from SNP LD mapping Hypothesis-free approach All markers vs. 1 trait

11. What we can learn from SNP LD mapping Hypothesis-free approach All markers vs. 1 trait

12. What we can learn from SNP LD mapping Hypothesis-free approach All markers vs. 1 trait

13. Set of Hypotheses Hypothesis-free approach All markers vs. 1 trait Almost all hypotheses are null. – They work as negative controls. – We obtain distribution under null hypothesis. A few hypotheses are “positive”. Pick up “outliers” from “null distribution” as positive signals

14. Many Positive Hypotheses Many hypotheses are “truly positive”. – Transctiptome, microarray-chips – False discovery rate

15. Many Positive Hypotheses Many hypotheses are “truly positive”. – Transctiptome, microarray-chips – False discovery rate

16. Many Positive Hypotheses Many hypotheses are “truly positive”. – Transctiptome, microarray-chips – False discovery rate

17. Many Positive Hypotheses Many hypotheses are “truly positive”. – Transctiptome, microarray-chips – False discovery rate Different approach to multiple testings from GWAS

18. Many Positive Hypotheses Many hypotheses are “truly positive”. – Transctiptome, microarray-chips – False discovery rate Similar approach to multiple testings in GWAS?

19. Many Positive Hypotheses Many hypotheses are “truly positive”. – Transctiptome, microarray-chips – False discovery rate Similar approach in GWAS? – e-QTL and cis-effect of neighboring SNPs on gene expression Nature Genetics 43, 561–564 (2011)

20. Both are GWAS but different Almost all hypotheses are null Many positive hypotheses

21. Both are GWAS but different Almost all hypotheses are null – All markers vs. 1 trait Many positive hypotheses – All markers vs. genes (traits)

22. Both are GWAS but different Almost all hypotheses are null – All markers vs. 1 trait Many positive hypotheses – All markers vs. genes (traits) Many traits

23. Both are GWAS but different Almost all hypotheses are null – All markers vs. 1 trait Many positive hypotheses – All markers vs. genes (traits) Many traits Positives : Neighboring and cis effects Negatives : Remote or trans effects

24. Many Positive Hypotheses with Rare Variant Analyses

25. Distribution of statistics is like what?

26. Many Positive Hypotheses with Rare Variant Analyses Distribution of statistics is like what? Strategy to detect signals should be cared.

27. Both are GWAS but different Almost all hypotheses are null – All markers vs. 1 trait Many Positive Hypotheses – All markers vs. genes (traits) Many traits Positives : Neighboring and cis effects Negatives : Remote or trans effects

28. Same GWAS but difference Almost all hypotheses are null – All markers vs. 1 phenotype Many Positive Hypotheses – All markers vs. genes (traits) Many traits Positives : Neighboring Negatives : Trans / Remote genes

29. Same GWAS but difference Almost all hypotheses are null – All markers vs. 1 phenotype Many Positive Hypotheses – All markers vs. cis-neighboring genes Many traits Positives : Neighboring Negatives : Trans / Remote genes Where are many traits?

30. Same GWAS but difference Almost all hypotheses are null – All markers vs. 1 phenotype Many Positive Hypotheses – All markers vs. cis-neighboring genes Many traits Positives : Neighboring Negatives : Trans / Remote genes Where are many traits? Biobank projects with multiple traits EHR (Electrical Health Record)-driven genetic studies – Very many phenotypes Nature Reviews Genetics 12, 417-428 (June 2011)

31. Same GWAS but difference Almost all hypotheses are null – All markers vs. 1 phenotype Many Positive Hypotheses – All markers vs. cis-neighboring genes Many traits Positives : Neighboring Negatives : Trans / Remote genes Where are many traits? Biobank projects with multiple traits EHR (Electrical Health Record)-driven genetic studies – Very many phenotypes Nature Reviews Genetics 12, 417-428 (June 2011)

32. Same GWAS but difference Almost all hypotheses are null – All markers vs. 1 phenotype Many Positive Hypotheses – All markers vs. cis-neighboring genes Many traits Positives : Neighboring Negatives : Trans / Remote genes Where are many traits? Physical / pathological conditions vary. Developmental stages vary. Cell types/ tissue types/ organs vary.

33. Same GWAS but difference Almost all hypotheses are null – All markers vs. 1 phenotype Many Positive Hypotheses – All markers vs. cis-neighboring genes Many traits Positives : Neighboring Negatives : Trans / Remote genes Where are many traits? Physical / pathological conditions vary. Developmental stages vary. Cell types/ tissue types/ organs vary.

34. Same GWAS but difference Many traits Positives : Neighboring Negatives : Trans / Remote genes Where are many traits? Physical / pathological conditions vary. Developmental stages vary. Cell types/ tissue types/ organs vary. Intra-individual diversity

35. Same GWAS but difference Many traits

36. Same GWAS but difference Many traits Change view points

37. Same GWAS but difference Many genotypes

38. Same GWAS but difference Many genotypes Where are many genotypes?

39. Same GWAS but difference Many genotypes Where are many genotypes? Physical / pathological conditions vary. Developmental stages vary. Cell types/ tissue types/ organs vary.

40. Same GWAS but difference Many genotypes Where are many genotypes? Physical / pathological conditions vary. Developmental stages vary. Cell types/ tissue types/ organs vary. Intra-individual diversity Again

41. Same GWAS but difference Many genotypes Where are many genotypes? Physical / pathological conditions vary. Developmental stages vary. Cell types/ tissue types/ organs vary. Intra-individual diversity Next generation sequencing technology makes these possible.

42. Changes by Next Generation Sequencing Technologies Individual cell-sequencing detects – Hereditary Variants from parents to offsprings – Non-hereditary but genetic de novo mutations in gamates Somatic mutations in fetus Somatic mutations after birth

43. Changes by Next Generation Sequencing Technologies Individual cell-sequencing detects – Hereditary Variants from parents to offsprings – Non-hereditary but genetic de novo mutations in gamates Somatic mutations in fetus Somatic mutations after birth Before birth After birth

44. Changes by Next Generation Sequencing Technologies Individual cell-sequencing detects – Hereditary Variants from parents to offsprings – Non-hereditary but genetic de novo mutations in gamates Somatic mutations in fetus Somatic mutations after birth Before birth After birth Hereditary ~ Genetic ~ Somatic Discriminations are becoming vague.

46. “Collapsing methods”

47. Patterns of possession of rare variants vary with phenotypes. “Collapsing methods” make the patterns into a style that can be tested easily; one-dimensional measure

48. “Collapsing methods” Patterns of possession of rare variants vary with phenotypes. “Collapsing methods” make the patterns into a style that can be tested easily; one-dimensional measure

49. “Collapsing methods” Patterns of possession of rare variants vary with phenotypes. “Collapsing methods” make the patterns into a style that can be tested easily; one-dimensional measure Information on functionality of variants might be used when collapse to modify the “measure”.

50. “Collapsing methods” Patterns of possession of rare variants vary with phenotypes. “Collapsing methods” make the patterns into a style that can be tested easily; one-dimensional measure Is this problem NEW to rare variant analyses?

51. “Collapsing methods” Patterns of possession of rare variants vary with phenotypes. “Collapsing methods” make the patterns into a style that can be tested easily; one-dimensional measure Is this problem NEW to rare variant analyses?

52. “Collapsing methods” Patterns of possession of rare variants vary with phenotypes. “Collapsing methods” make the patterns into a style that can be tested easily; one-dimensional measure Is this problem NEW to rare variant analyses? – NO

53. “Collapsing methods” Patterns of possession of rare variants vary with phenotypes. “Collapsing methods” make the patterns into a style that can be tested easily; one-dimensional value Is this problem NEW to rare variant analyses? – We do NOT have adequate solutions for patterns of common variants. Genetics September 2007 vol. 177 no. 1 535-547

54. “Collapsing methods” Patterns of possession of rare variants vary with phenotypes. “Collapsing methods” make the patterns into a style that can be test easily; one-dimensional value Is this problem NEW to rare variant analyses? – We do NOT have adequate solutions for patterns of common variants. How to handle patterns of variants, BOTH common and rare, should be solved.

55. Patterns of variants

56. Rephrase as “Genetic heterogeneity PER GENES”

57. Notes on Genetic Heterogeneity PER GENES Genes vary in their size.

58. Notes on Genetic Heterogeneity PER GENES Genes vary in their size.

59. Notes on Genetic Heterogeneity PER GENES Genes vary in their size. Number of variants vary among genes.

60. Notes on Genetic Heterogeneity PER GENES Genes vary in their size. Number of variants vary among genes. Allele frequencies vary among genes.

61. Notes on Genetic Heterogeneity PER GENES Genes vary in their size. Number of variants vary among genes. Allele frequencies vary among genes. Haplotype frequencies vary among genes.

62. Notes on Genetic Heterogeneity PER GENES Genes vary in their size. Number of variants vary among genes. Allele frequencies vary among genes. Haplotype frequencies vary among genes. Functional variations due to genetic variants vary among genes.

63. Unsolved Issues in Common Variant-GWAS on Genetic Heterogeneity PER GENES GWAS

64. Unsolved Issues in Common Variant-GWAS on Genetic Heterogeneity PER GENES GWAS

65. Unsolved Issues in Common Variant-GWAS on Genetic Heterogeneity PER GENES GWAS – Hypothesis-free approach, targetting genes evenly

66. Unsolved Issues in Common Variant-GWAS on Genetic Heterogeneity PER GENES GWAS – Hypothesis-free approach, targetting genes evenly – But...

67. Unsolved Issues in Common Variant-GWAS on Genetic Heterogeneity PER GENES GWAS – Hypothesis-free approach, targetting genes evenly – But... – All genes are studied UNEVENLY.

68. Unsolved Issues in Common Variant-GWAS on Genetic Heterogeneity PER GENES GWAS – Hypothesis-free approach, targetting genes evenly – But... – All genes are studied UNEVENLY. Genetic heterogeneity PER GENES affects on sensitivity and specificity HLA region and its extreme heterogeneity...

69. Unsolved Issues in Common Variant-GWAS on Genetic Heterogeneity PER GENES GWAS – Hypothesis-free approach, targetting genes evenly – But... – All genes are studied UNEVENLY. Genetic heterogeneity PER GENES affects on sensitivity and specificity HLA region and its extreme heterogeneity... Am J Hum Genet. 2005 April; 76(4): 634–646.

70. UNEVENNESS Genetic Importance GWAS identifies genes of “Genetic Importance” that – are Important for a phenotype, – have functional variants.

71. UNEVENNESS Genetic Importance GWAS identifies genes of “Genetic Importance” that – are Important for a phenotype, – have functional variants.

72. UNEVENNESS Genetic Importance GWAS identifies genes of “Genetic Importance” that – are Important for a phenotype, – have functional variants.

73. UNEVENNESS Genetic Importance GWAS identifies genes of “Genetic Importance” that – are Important for a phenotype, – have functional variants.

74. UNEVENNESS Genetic Importance Consider a pathway that is important for a phenotype. – Gene X in the pathway is functionally critical without genetic variants. – Gene Y in the pathway has some functional roles with genetic variants that make phenotype variations. Gene X is “Functionally” important. Gene Y is “Genetically” important. http://www.genome.jp/kegg/

75. UNEVENNESS Genetic Importance Consider a pathway that is important for a phenotype. – Gene X in the pathway is functionally critical without genetic variants. – Gene Y in the pathway has some functional roles with genetic variants that make phenotype variations. Gene X is “Functionally” important. Gene Y is “Genetically” important. http://www.genome.jp/kegg/

76. UNEVENNESS Genetic Importance Consider a pathway that is important for a phenotype. – Gene X in the pathway is functionally critical without genetic variants. – Gene Y in the pathway has some functional roles with genetic variants that make phenotype variations. Gene X is “Functionally” important. Gene Y is “Genetically” important. http://www.genome.jp/kegg/

77. UNEVENNESS Genetic Importance Consider a pathway that is important for a phenotype. – Gene X in the pathway is functionally critical without genetic variants. – Gene Y in the pathway has some functional roles with genetic variants that make phenotype variations. Gene X is “Functionally” important. Gene Y is “Genetically” important.

78. UNEVENNESS Genetic Importance Consider a pathway that is important for a phenotype. – Gene X in the pathway is functionally critical without genetic variants. – Gene Y in the pathway has some functional roles with genetic variants that make phenotype variations. Gene X is “Functionally” important. Gene Y is “Genetically” important.

79. UNEVENNESS Genetic Importance Consider a pathway is important for a phenotype. – Gene X in the pathway is functionally critical without genetic variants. – Gene Y in the pathway has some functional role with genetic variants that make phenotype variations. Gene X is “Functionally” important. Gene Y is “Genetically” important. Genetic studies work only on “genetic” importance.

80. Genetic Heterogeneity PER GENES determines “Genetic Importance” We do NOT have adequate methods to measure genetic heterogeneity per genes.

81. Genetic Heterogeneity PER GENES determines “Genetic Importance” But,...

82. Genetic Heterogeneity PER GENES determines “Genetic Importance” We do NOT have adequate methods to measure genetic heterogeneity per genes.

83. Genetic Heterogeneity PER GENES determines “Genetic Importance” We do NOT have adequate methods to measure genetic heterogeneity per genes. One-dimensional measures Mean Variance

84. Genetic Heterogeneity PER GENES determines “Genetic Importance” We do NOT have adequate methods to measure genetic heterogeneity per genes. Genetics September 2007 vol. 177 no. 1 535-547 One-dimensional measures Mean Variance

85. Genetic Heterogeneity PER GENES determines “Genetic Importance” We do NOT have adequate methods to measure genetic heterogeneity per genes. Genetics September 2007 vol. 177 no. 1 535-547 One-dimensional measures Mean Variance How to measure?

86. Genetic Heterogeneity PER GENES For Species Or For Individuals

87. Genetic Heterogeneity PER GENES For Populations Or For Individuals

88. Heterogeneity in an absolute scale

89. Genetic Heterogeneity PER GENES For Populations Or For Individuals Heterogeneity in an absolute scale

90. Genetic Heterogeneity PER GENES For Populations Or For Individuals Heterogeneity in an absolute scale Heterogeneity in a relative scale inside of individuals

91. Genetic Heterogeneity PER GENES For Populations Or For Individuals Heterogeneity in an absolute scale Heterogeneity in a relative scale inside of individuals Intra-individul genetic heterogeneity

92. Intra-individul Genetic Heterogeneity They are the source of phenotypic heterogeneity.

93. Intra-individul Genetic Heterogeneity They are the source of phenotypic heterogeneity. Individuals have to show phenotypic variations that are the basics of living creatures.

94. Intra-individul Genetic Heterogeneity They are the source of phenotypic heterogeneity. Individuals have to show phenotypic variations that are the basics of living creatures. Individual genetic heterogeneity is the resource for the individual to realize phenotypic variations.

95. Intra-individul Genetic Heterogeneity Individual genetic heterogeneity is the resource for the individual to realize phenotypic variations. Genetic heterogeneity in a gene for an individual may be relatively more important than others, depending on other genes’ heterogeneity context.

96. Intra-individul Genetic Heterogeneity Individual genetic heterogeneity is the resource to realize phenotypic variations by the individual. Genetic heterogeneity in a gene for an individual may be relatively more important than others, depending on other genes’ heterogeneity context. G x G interaction

97. G x G with rare variants G x G approach in general – Gene set in a functional pathway – Problems with too many combinations Same both for common variants and rare variants – Many and scarce variants might G x G approach less realistic. – Scarce but Many variants could open new ways to G x G approach ??

98. G x G with rare variants G x G approach in general – Gene set in a functional pathway – Problems with too many combinations Same both for common variants and rare variants – Many and scarce variants might G x G approach less realistic. – Scarce but Many variants could open new ways to G x G approach ??

99. G x G with rare variants G x G approach in general – Gene set in a functional pathway – Problems with too many combinations Same both for common variants and rare variants – Many and scarce variants might make G x G approach less realistic. – Scarce but Many variants could open new ways to G x G approach ??

100. G x G with rare variants G x G approach in general – Gene set in a functional pathway – Problems with too many combinations Same both for common variants and rare variants – Many and scarce variants might make G x G approach less realistic. – Scarce but Many variants could open new ways to G x G approach with new measures?????

101. Summary Next generation sequencing enabled rare variant analyses. Use what next generation sequencing brought us. – Expansion of phenotypes and genotypes – Set of hypotheses and multiple testing – Measures of genetic heterogeneity – Intra-individual heterogeneities

102. Thank you

103. Announcement Symposium and Data Analysis Hands-on Seminar Jan. 16 – Jan. 19, 2013 @ Kyoto University, Kyoto, Japan Organized by Marc Lathrop @ CEPH Jurg Ott @ Rockefeller Univ. Fumihiko Matsuda @ Kyoto Univ. Details will be announced shortly. If you are interested in this seminar or need updated timely for the details or registration, e-mail to: ryamada@genome.med.kyoto-u.ac.jp (Ryo Yamada, Kyoto Univ.)ryamada@genome.med.kyoto-u.ac.jp

104. When phenotypes are different Distribution of statistics is different, Strategy to identify positive signals is different.

105. Other comments Brush-up Phenotypes Quantity of phenotypes and quality of phenotypes. Phenotypes should be more directly related to genetic variants. Variance of Phenotypes should be larger. Function in a regular condition and in a stressed condition.

106. Changes with massively-parallel sequencing From hereditary factors to “genetic” factors Hierarchy in genetic heterogeneity